CN110034561A - A kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service - Google Patents
A kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service Download PDFInfo
- Publication number
- CN110034561A CN110034561A CN201910404131.9A CN201910404131A CN110034561A CN 110034561 A CN110034561 A CN 110034561A CN 201910404131 A CN201910404131 A CN 201910404131A CN 110034561 A CN110034561 A CN 110034561A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power plant
- cost
- monthly
- cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 241
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 title claims abstract description 21
- 230000005611 electricity Effects 0.000 claims abstract description 39
- 238000005553 drilling Methods 0.000 claims description 12
- 238000012423 maintenance Methods 0.000 claims description 12
- 230000005684 electric field Effects 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 6
- 230000003542 behavioural effect Effects 0.000 claims description 5
- 230000008901 benefit Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 230000000295 complement effect Effects 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 210000000349 chromosome Anatomy 0.000 description 5
- 230000006872 improvement Effects 0.000 description 4
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H02J3/386—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/10—Energy trading, including energy flowing from end-user application to grid
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Finance (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Computational Linguistics (AREA)
- Marketing (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention discloses a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service, and step includes: the behavior of S1. prediction wind power plant user operating lease energy storage, obtains cloud energy storage service price；S2. stored energy capacitance optimization object function is determined to predict that the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, short of electricity punishment cost minimize, it is less than the monthly cost of the self-built energy storage of wind power plant as constraint condition using the monthly lease service price of cloud energy storage, meet wind-powered electricity generation fluctuation and stabilize rate, the wind power plant lease optimal monthly capacity of energy storage is configured；S3. cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to the preliminary foundation of energy storage leasing market price, rule are designed.Implementation method of the present invention is simple, cloud energy storage business model and technological service have higher economy and validity.
Description
Technical field
The present invention relates to New-energy power system technical field more particularly to a kind of wind-powered electricity generations based on cloud energy storage lease service
Field energy storage capacity optimization method.
Background technique
As wind-powered electricity generation permeability increases, while wind-powered electricity generation is that electric system exports a large amount of inexpensive clean energy resourcies, wind-powered electricity generation is solid
Some fluctuations, the intermittent influence to Power System Reliability, stability are also growing day by day.In recent years many scholars are to energy storage
It stabilizes wind-powered electricity generation fluctuation problem to be studied, achieves many achievements.For example, defining wind power plant schedulability is used as constraint,
Meter and discharge process life of storage battery detraction establish objective function, realize and consider that the wind power plant of battery economy is schedulable
Property；State-of-charge partition model is proposed to adjust battery charging and discharging power in real time, with wind power plant investment maintenance cost, operation at
Originally, the out-of-limit cost minimization of battery realizes wind-powered electricity generation fluctuation for objective function and stabilizes；Strategy is stabilized to difference by emulation mode, no
Same grid-connected mode, different fluctuations are stabilized the configuration of the wind power plant energy storage under reliability and are studied；Wind-powered electricity generation output is analyzed in time domain
With the fluctuation characteristic of frequency domain, extract fluctuation degree is indicated with quantification index QI (Quantization Index), is matched based on QI cluster
Optimal energy storage power capacity is set.
The above method plays an important role to wind storage system economy is improved, but investing entities energy storage cost is still higher,
Do not have a possibility that large-scale promotion.The defects of for energy storage device higher cost, it is thus proposed that carry out the concept of cloud energy storage.Cloud
Energy storage provider is to meet user's energy storage to lease demand, the extensive energy storage device of investment construction, and will be dispersed in the spare time of user side
It sets energy storage to put together, substitutes user subject energy storage with the stored energy capacitance of cloud virtual, be the depth of shared economy and electric system
Degree fusion.
Cloud energy storage is as electric system neomorph, and wind power plant, Demand-side etc. are likely to purchase cloud energy storage clothes in the near future
It is engaged in or selects self-built energy storage.For this purpose, the present invention devises cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, invention
A kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service.
Summary of the invention
The technical problem to be solved in the present invention is that, for technical problem of the existing technology, the present invention provides one
Kind implementation method is simple, can fluctuate and stabilize effective for wind-powered electricity generation, can preferably realize the method that wind power accurately controls.Root
According to the cost structure of cloud energy storage operator, the monthly value of leass of wind power plant cloud energy storage is predicted using statistical method, reduces wind
Electric field energy storage cost.
In order to solve the above technical problems, technical solution proposed by the present invention are as follows:
A kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service, step include:
S1. the behavior for predicting wind power plant user operating lease energy storage, obtains cloud energy storage service price；
S2. to predict the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, short of electricity punishment cost
It minimizes and determines stored energy capacitance optimization object function, it is monthly to be less than the self-built energy storage of wind power plant with the monthly lease service price of cloud energy storage
Cost is constraint condition, meets wind-powered electricity generation fluctuation and stabilizes rate, is configured to the wind power plant lease optimal monthly capacity of energy storage；
S3. design cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to energy storage leasing market price,
The preliminary foundation of rule.
As a further improvement of the present invention: in the step S1, predict the behavior of wind power plant user operating lease energy storage,
Obtain cloud energy storage service price.Complementary effect and scale and benefit between user, cloud energy storage investment, maintenance are converted into monthly
Cost is relatively low, but the bad habit of user's operating lease energy storage will increase the cost of cloud energy storage operator.
As a further improvement of the present invention: steps are as follows for the specific calculating of cloud energy storage service price:
S11. the investment totle drilling cost of cloud energy storage and monthly maintenance cost are as follows:
Cess=α CmCap
Com=α CmvCap
Wherein, Cess、ComCost of investment, maintenance cost for cloud energy storage, α are that the cost of cloud energy storage reduces coefficient, α < 1；
CmFor unit stored energy capacitance system cost；CapFor the stored energy capacitance of the quasi- purchase of wind power plant u；CmvFor the dimension of energy-storage system unit monthly
Shield expense.
S12. because user's super-charge super-discharge, frequent charge and discharge will lead to energy storage service life detraction, roll over cloud energy storage overall cost of ownership
It is bonded to monthly cost to increase, the behavioural habits of wind power plant u operating lease energy storage need to be investigated.Wind power plant u monthly calls lease to store up
The behavioural habits of energy are related to of that month wind-powered electricity generation fluctuation situation, prediction accuracy, i.e. the charge and discharge behavior of wind power plant every month u is to become
Change, it is also variation that cloud energy storage overall cost of ownership, which is converted into monthly cost,.Assuming that by wind in the life cycle management of energy storage
Electric field u is used, and the service life of final energy storage is Tlife,u.Then cloud energy storage provides energy storage service average throwing monthly for wind power plant u
Provide cost are as follows:
The monthly totle drilling cost of cloud energy storage are as follows:
Cces,u=Cess,u+Com
If T can be estimatedlife,u, cloud energy storage can be obtained as wind power plant user u, the average monthly assembly of lease service is provided
This.Consider that cloud energy storage is to reduce operations risks, takes life cycle management T in the similar wind power plant in Building Nlife,iMaximum n family's wind power plant
Life cycle management T of the average as wind power plant ulife,u, such as following formula:
It S13. is to obtain the energy storage for wind power plant user u multiplied by profit coefficient on the basis of cloud energy storage monthly totle drilling cost
Monthly lease service price:
Fces=(1+ β) Cces,u
In formula, FcesFor the monthly lease service price of cloud energy storage, β is profit coefficient.
As a further improvement of the present invention: to predict the monthly lease service valence of resulting cloud energy storage in the step S2
Lattice, wind power plant abandonment punishment cost, short of electricity punishment cost, which minimize, determines stored energy capacitance optimization object function, monthly with cloud energy storage
It is constraint condition that lease service price, which is less than the monthly cost of the self-built energy storage of wind power plant, meets wind-powered electricity generation fluctuation and stabilizes rate, to wind power plant
The lease optimal monthly capacity of energy storage is configured.The establishment step of objective function is as follows:
Cost is lost in abandonment punishment cost and smooth power shortage:
FLT=ρLLLT
FLT=ρLLLT
In formula: LLT、FLTTo install this month abandonment electricity, punishment cost after energy-storage system, L additionalST、FSTFor smooth power shortage
Electricity, loss cost；PMSCombine for wind storage system and contributes；Eup、EdownIt is upper and lower that power output the permitted maximum range is fluctuated for wind power plant
Limit；n1、n2The respectively number of the number of wind power plant abandonment, flat volatility underpower；t1, t2Respectively wind power plant abandonment is opened
Beginning, the end time of beginning, end time or smooth power shortage；ρL、ρSRespectively wind power plant abandonment energy loss, smooth function
The corresponding unit price of rate shortage energy.
Objective function are as follows:
min(Fces+FLT+FST)
As a further improvement of the present invention: assuming that the monthly cost of the self-built energy storage of wind power plant u is FBess,u, with cloud energy storage
It is constraint that monthly lease service price, which is less than the monthly cost of the self-built energy storage of wind power plant:
FBess,u< Fces
As a further improvement of the present invention: output of wind electric field fluctuation constraint:
P{|ΔPd(t)|≤ΔPd max}≥Λ
Wherein, P { } is probability-distribution function；ΔPd{ t } is wind power plant-cloud energy storage joint power output undulating value；ΔPd max
For undulating value the permitted maximum range upper limit；Λ is level of confidence.
The optimal value of the monthly configuration of cloud stored energy capacitance is solved based on genetic algorithm.Cloud energy storage initial stage business model is devised,
That is stored energy capacitance lease service, in order to the preliminary foundation of energy storage leasing market price, rule.
Compared with the prior art, the advantages of the present invention are as follows:
1) cloud energy storage is as electric system neomorph, and wind power plant, Demand-side etc. are likely to purchase cloud energy storage in the near future
Service or select self-built energy storage.For this purpose, the present invention devises cloud energy storage initial stage business model, i.e. stored energy capacitance lease service.With
The bad habit of family operating lease energy storage will lead to cloud energy storage operator and generate extra cost, additionally be subtracted using the energy storage monthly service life
Damage amount is measured.Amount estimation method is additionally detracted by the monthly service life, obtains the monthly rent of cloud energy storage for wind power plant user
It rents price.Under the premise of meeting the fluctuation of certain wind-powered electricity generation and stabilizing effect, with predict the monthly lease service price of resulting cloud energy storage,
Wind power plant abandonment punishment cost, the minimum objective function of short of electricity punishment cost are less than wind with the monthly lease service price of cloud energy storage
The monthly cost of the self-built energy storage of electric field is constraint, establishes the Optimized model of wind power plant cloud energy storage configuration, obtains monthly optimal cloud storage
It can configuration capacity.
2) present invention by the novel operation mode of cloud energy storage be used for wind-powered electricity generation fluctuation stabilize, can preferably realize that wind power is accurate
Control.Designed cloud energy storage initial stage business model facilitates the preliminary foundation of energy storage leasing market price, rule.
3) present invention predicts the wind power plant cloud energy storage moon using statistical method according to the cost structure of cloud energy storage operator
Spend value of leass.Wind power plant lease energy storage service can largely be reduced compared to self-built energy storage while guaranteeing that wind-powered electricity generation stabilizes effect
Cost.
4) present invention configures optimal lease energy storage according to of that month wind-powered electricity generation degree of fluctuation in wind power plant, fixes compared to self-built energy storage
Stored energy capacitance substantially increase configuration energy storage specific aim and flexibility.
Detailed description of the invention
Fig. 1 is the implementation process signal of wind farm energy storage capacity optimization method of the present embodiment based on cloud energy storage lease service
Figure.
Fig. 2 is the practical power output schematic diagram of wind power plant in the specific embodiment of the invention.
Specific embodiment
Below in conjunction with Figure of description and specific preferred embodiment, the invention will be further described, but not therefore and
It limits the scope of the invention.
As shown in Figure 1, wind farm energy storage capacity optimization method of the present embodiment based on cloud energy storage lease service, step packet
It includes:
S1. the behavior for predicting wind power plant user operating lease energy storage, obtains cloud energy storage service price；
S2. to predict the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, short of electricity punishment cost
It minimizes and determines stored energy capacitance optimization object function, it is monthly to be less than the self-built energy storage of wind power plant with the monthly lease service price of cloud energy storage
Cost is constraint condition, meets wind-powered electricity generation fluctuation and stabilizes rate, is configured to the wind power plant lease optimal monthly capacity of energy storage；
S3. design cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to energy storage leasing market price,
The preliminary foundation of rule.
The present embodiment stabilizes problem for wind power output fluctuation, using the novel business model of purchase cloud energy storage, realizes wind
It is controllable that electric field goes out activity of force.Cloud energy storage has polymerize the control information of a large amount of distributed energy storages and centralized energy storage, can be wind power plant
Energy storage lease service is provided, cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to energy storage rent are thus devised
The preliminary foundation for the market price, rule of renting.The cost structure of cloud energy storage operator is studied, in order to avoid user's super-charge super-discharge etc. is given
Cloud energy storage brings extra cost, has obtained cloud energy storage service valence based on the prediction to wind power plant user's operating lease energy storage behavior
Lattice.To predict the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, the minimum mesh of short of electricity punishment cost
Scalar functions, being less than the monthly cost of the self-built energy storage of wind power plant with the monthly lease service price of cloud energy storage is constraint, meets wind-powered electricity generation fluctuation
Rate is stabilized, the wind power plant lease optimal monthly capacity of energy storage is configured.The mentioned cloud energy storage business of simulation analysis result verification
The economy and validity of mode and technological service.
In the present embodiment, it is primarily based in step S1 and cloud has been obtained to the prediction of wind power plant user's operating lease energy storage behavior
Energy storage service price.Cloud energy storage operation initial stage limitation user's short-term lease determines that wind power plant user cannot be daily even per several small
Shi Genghuan stored energy capacitance and power, to cooperate ultra-short term prediction to do optimizing decision, present embodiment assumes that wind power plant is monthly bought
Cloud energy storage service.Because of the complementary effect and scale and benefit between user, cloud energy storage investment, maintenance convert into monthly cost compared with
It is low, but the bad habit of user's operating lease energy storage will increase the cost of cloud energy storage operator.It is with battery energy storage system BESS
Example, the monthly cost of BESS are influenced by energy storage operating ambient temperature, depth of discharge, charge and discharge number.User can be free
Determine charge and discharge number, the depth of discharge of lease energy storage, the variation of user's charge and discharge strategy, the difference of charge and discharge number can
The service life for influencing BESS, to influence the cost of cloud energy storage operator.And the energy storage working environment temperature that cloud energy storage is polymerize
Degree condition is good, and temperature factor can ignore cloud energy storage cost impact very little.
In the present embodiment, the investment totle drilling cost of cloud energy storage and monthly maintenance cost are as follows:
Cess=α CmCap (1)
Com=α CmvCap (2)
Wherein, Cess、ComBehavioural habits and this month of lease energy storage are monthly called for the cost of investment wind power plant u of cloud energy storage
It is related that wind-powered electricity generation fluctuates situation, prediction accuracy, so the charge and discharge behavior of wind power plant u every month is variation, cloud energy storage is always thrown
It is also variation that money cost, which is converted into monthly cost,., maintenance cost, α be cloud energy storage cost reduce coefficient, α < 1；CmFor
Unit stored energy capacitance system cost；CapFor the stored energy capacitance of the quasi- purchase of wind power plant u；CmvFor the maintenance of energy-storage system unit monthly
Expense；
In the present embodiment, since user's super-charge super-discharge, frequent charge and discharge will lead to energy storage service life detraction, throw cloud energy storage always
Money cost is converted into monthly cost raising, need to investigate the behavioural habits of wind power plant u operating lease energy storage.Because obtaining cloud energy storage
Average monthly taxi cost, it is assumed that used in the life cycle management of energy storage by user u, the service life of final energy storage is
Tlife,u.Then cloud energy storage provides energy storage service average cost of investment monthly for wind power plant u are as follows:
The monthly totle drilling cost of cloud energy storage are as follows:
Cces,u=Cess,u+Com (4)
By formula (1)-(4) it is found that if T can be estimatedlife,u, cloud energy storage can be obtained as wind power plant user u, lease clothes is provided
The average monthly totle drilling cost of business.For the specific aim price for realizing wind power plant user u, the N that there is similar characteristic with wind power plant u is obtained
The life cycle management of seat wind power plant operating lease energy storage is as sample.There is no with the duplicate wind power plant of wind power plant u, for protect
Card sample size N is sufficiently large, need to extract the principal element for influencing power swing.The present embodiment takes and wind power plant u gas having the same
It waits, topography, wind power plant of the installed capacity difference no more than 10% is as sample.
Consider that cloud energy storage is to reduce operations risks, takes life cycle management T in the similar wind power plant in Building Nlife,iMaximum n family tradition
Life cycle management T of the average of electric field as wind power plant ulife,u, as shown in formula (5):
In the present embodiment, obtained multiplied by profit coefficient for wind power plant user on the basis of cloud energy storage monthly totle drilling cost
The monthly lease service price of the energy storage of u:
Fces=(1+ β) Cces,u (6)
In formula, FcesFor the monthly lease service price of cloud energy storage, β is profit coefficient.
In the present embodiment S2, aiming at for wind farm energy storage capacity optimization meets under the smooth constraint of power swing, makes
Totle drilling cost is minimum, including the monthly lease service price of the resulting cloud energy storage of prediction, abandonment punishment cost, short of electricity punishment cost, with
Minimum cost realizes the operation of wind power plant Optimum Economic.
1. objective function
Cost is lost in abandonment punishment cost and smooth power shortage:
FLT=ρLLLT (9)
FST=ρSLST (10)
In formula: LLT、FLTTo install this month abandonment electricity, punishment cost after energy-storage system, L additionalST、FSTFor smooth power shortage
Electricity, loss cost；PMSCombine for wind storage system and contributes；Eup、EdownIt is upper and lower that power output the permitted maximum range is fluctuated for wind power plant
Limit；n1、n2The respectively number of the number of wind power plant abandonment, flat volatility underpower；t1, t2Respectively wind power plant abandonment is opened
Beginning, the end time of beginning, end time or smooth power shortage；ρL、ρSRespectively wind power plant abandonment energy loss, smooth function
The corresponding unit price of rate shortage energy.
Objective function are as follows:
min(Fces+FLT+FST) (11)
2. constraint condition
Assuming that the monthly cost of the self-built energy storage of wind power plant u is FBess,u, wind-powered electricity generation is less than with the monthly lease service price of cloud energy storage
The self-built monthly cost of energy storage in field is constraint:
FBess,u< Fces (12)
Output of wind electric field fluctuation constraint:
P{|ΔPd(t)|≤ΔPd max}≥Λ (13)
Wherein, P { } is probability-distribution function；ΔPd{ t } is wind power plant-cloud energy storage joint power output undulating value；ΔPd max
For undulating value the permitted maximum range upper limit；Λ is level of confidence；
3. method for solving
The present embodiment is based on genetic algorithm and solves optimal models, the specific steps are as follows:
Step 1: the input initial data such as wind power plant and the monthly service charge of cloud energy storage unit capacity, write-in constraint condition.
Step 2: being coding form by variables transformations, initial chromosome is obtained by coding.
Step 3: acquiring each chromosome fitness function value.Pass through breeding, intersection, 3 kinds of generations of variation chromosome of new generation
Domain, and its new adaptive value is calculated after decoding to next-generation chromosome.
Step 4: presetting hereditary number；If being unsatisfactory for equality constraint and inequality constraints and variable bound range
, it returns immediately.
Step 5: obtaining the best solution of fitness in chromosome, the i.e. optimal value of the monthly configuration of cloud stored energy capacitance.
In order to verify the validity of the present embodiment above method, complete certain wind power plant 1- in 2014 of force data is gone out with history
6 months actual operating datas calculate the purchase optimal capacity of cloud energy storage.The wind energy turbine set installed capacity is 100MW, the practical fortune of wind power plant
Row data such as Fig. 2 is divided into 10min between acquisition time.60% that cloud energy storage cost is current entity energy storage cost is set, i.e. cloud stores up
It is 60% that the cost of energy, which reduces factor alpha, and setting profit factor beta is 10%.
Assuming that cloud energy storage provider obtains the monthly additional service life detraction Δ T of wind power plant u with above-mentioned method estimationuFor
0.23 month, it can thus be concluded that the monthly lease service unit of value capacity price of cloud energy storage is 5778USD/MWh.Wind power plant is according to the moon
Degree lease service price and this month itself, go out force data, calculate optimal cloud stored energy capacitance, 6 months wind power plant cloud stored energy capacitances of gained
Configuring condition is as shown in table 1.
1 calculated result of table
To extend the energy storage service life when wind power plant uses self-built energy storage, charge and discharge range is traditionally arranged to be 20%-80% maximum
Stored energy capacitance, and charge and discharge range constraint is generally not provided with when operating lease energy storage.Assuming that self-built stored energy capacitance also can be flexible
Variation, amount of capacity and cloud stored energy capacitance monthly is in the same size, to the unit capacity moon of 6 self-built energy storage of middle of the month wind power plant
Degree price is averaged, and 7808USD/MWh is obtained.The monthly unit capacity price of i.e. self-built energy storage is 7808USD/MWh, wind-powered electricity generation
The studio rent monthly unit capacity cost of cloud energy storage of renting can save 2030USD/MWh in contrast.According to table 1, energy storage lease is held
Amount can fluctuate situation with wind-powered electricity generation month and flexibly change, and the stored energy capacitance that self-built energy storage is fixed is likely to cause certain months storages
It can capacity excess or deficiency.In conclusion cost is relatively low and cloud stored energy capacitance can flexibly track output of wind electric field wave for cloud energy storage
Dynamic seasonality.
Above-mentioned only presently preferred embodiments of the present invention, is not intended to limit the present invention in any form.Although of the invention
It has been disclosed in a preferred embodiment above, however, it is not intended to limit the invention.Therefore, all without departing from technical solution of the present invention
Content, technical spirit any simple modifications, equivalents, and modifications made to the above embodiment, should all fall according to the present invention
In the range of technical solution of the present invention protection.
Claims (6)
1. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service, which is characterized in that step includes:
S1. the behavior for predicting wind power plant user operating lease energy storage, obtains cloud energy storage service price；
S2. to predict that the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, short of electricity punishment cost are minimum
Change and determine stored energy capacitance optimization object function, the monthly cost of the self-built energy storage of wind power plant is less than with the monthly lease service price of cloud energy storage
For constraint condition, meet wind-powered electricity generation fluctuation and stabilize rate, the wind power plant lease optimal monthly capacity of energy storage is configured；
S3. cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to energy storage leasing market price, rule are designed
Preliminary foundation.
2. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service according to claim 1, special
Sign is, in the step S1, predicts the behavior of wind power plant user operating lease energy storage, obtains cloud energy storage service price.User
Between complementary effect and scale and benefit, cloud energy storage investment, maintenance convert into monthly that cost is relatively low, but user's operating lease
The bad habit of energy storage will increase the cost of cloud energy storage operator.
3. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service according to claim 2, special
Sign is that steps are as follows for the specific calculating of cloud energy storage service price:
S11. the investment totle drilling cost of cloud energy storage and monthly maintenance cost are as follows:
Cess=α CmCap
COm=αCmvCap
Wherein, Cess、ComCost of investment, maintenance cost for cloud energy storage, α are that the cost of cloud energy storage reduces coefficient, α < 1；CmFor
Unit stored energy capacitance system cost；CapFor the stored energy capacitance of the quasi- purchase of wind power plant u；CmvFor the maintenance of energy-storage system unit monthly
Expense.
S12. because user's super-charge super-discharge, frequent charge and discharge will lead to the energy storage service life detraction, make cloud energy storage overall cost of ownership convert into
Monthly cost increases, and need to investigate the behavioural habits of wind power plant u operating lease energy storage.Wind power plant u monthly calls lease energy storage
Behavioural habits are related to of that month wind-powered electricity generation fluctuation situation, prediction accuracy, i.e. the charge and discharge behavior of wind power plant every month u is variation
, it is also variation that cloud energy storage overall cost of ownership, which is converted into monthly cost,.Assuming that by wind-powered electricity generation in the life cycle management of energy storage
Field u is used, and the service life of final energy storage is Tlife,u.Then cloud energy storage provides energy storage service average investment monthly for wind power plant u
Cost are as follows:
The monthly totle drilling cost of cloud energy storage are as follows:
Cces,u=Cess,u+Com
If T can be estimatedlife,u, cloud energy storage can be obtained as wind power plant user u, the average monthly totle drilling cost of lease service is provided.
Consider that cloud energy storage is to reduce operations risks, takes life cycle management T in the similar wind power plant of Building N operation conditionslife,iMaximum n family
Life cycle management T of the average of wind power plant as wind power plant ulife,u, such as following formula:
S13. the energy storage for obtaining being directed to wind power plant user u multiplied by profit coefficient on the basis of cloud energy storage monthly totle drilling cost is monthly
Lease service price:
Fces=(1+ β) Cces,u
In formula, FcesFor the monthly lease service price of cloud energy storage, β is profit coefficient.
4. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service according to claim 1, special
Sign is, the monthly lease service price of resulting cloud energy storage, wind power plant abandonment punishment cost, short of electricity are predicted in the step S2
Punishment cost, which minimizes, determines stored energy capacitance optimization object function, and it is self-built to be less than wind power plant with the monthly lease service price of cloud energy storage
The monthly cost of energy storage is constraint condition, meets wind-powered electricity generation fluctuation and stabilizes rate, is matched to the wind power plant lease optimal monthly capacity of energy storage
It sets.The establishment step of objective function is as follows:
Cost is lost in abandonment punishment cost and smooth power shortage:
FLT=ρLLLT
FLT=ρLLLT
In formula: LLT、FLTTo install this month abandonment electricity, punishment cost after energy-storage system, L additionalST、FSTFor smooth power shortage electricity,
Lose cost；PMSCombine for wind storage system and contributes；Eup、EdownPower output the permitted maximum range upper and lower limit is fluctuated for wind power plant；n1、
n2The respectively number of the number of wind power plant abandonment, flat volatility underpower；t1, t2Respectively wind power plant abandonment starts, terminates
The beginning of time or smooth power shortage, end time；ρL、ρSRespectively wind power plant abandonment energy loss, smooth power shortage energy
The corresponding unit price of amount.
Objective function are as follows:
min(Fces+FLT+FST)。
5. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service according to claim 4, special
Sign is, it is assumed that the monthly cost of the self-built energy storage of wind power plant u is FBess,u, wind-powered electricity generation is less than with the monthly lease service price of cloud energy storage
The self-built monthly cost of energy storage in field is constraint:
FBess,u< Fces
Output of wind electric field fluctuation constraint:
P{|ΔPd(t)|≤ΔPdmax}≥Λ
Wherein, P { } is probability-distribution function；ΔPd{ t } is wind power plant-cloud energy storage joint power output undulating value；ΔPdmaxFor fluctuation
It is worth the permitted maximum range upper limit；Λ is level of confidence.
6. a kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service described in -5 according to claim 1,
It is characterized in that, designs cloud energy storage initial stage business model, i.e. stored energy capacitance lease service, in order to energy storage leasing market price, rule
Preliminary foundation then.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910404131.9A CN110034561B (en) | 2019-05-15 | 2019-05-15 | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910404131.9A CN110034561B (en) | 2019-05-15 | 2019-05-15 | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110034561A true CN110034561A (en) | 2019-07-19 |
| CN110034561B CN110034561B (en) | 2022-10-11 |
Family
ID=67242259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910404131.9A Active CN110034561B (en) | 2019-05-15 | 2019-05-15 | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110034561B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111864770A (en) * | 2020-08-19 | 2020-10-30 | 国网河南省电力公司电力科学研究院 | Energy storage auxiliary frequency modulation scheduling method based on cloud energy storage |
| CN112016824A (en) * | 2020-08-25 | 2020-12-01 | 国网四川省电力公司经济技术研究院 | Energy storage resource matching method based on shared economy concept |
| CN113592553A (en) * | 2021-08-02 | 2021-11-02 | 广西大学 | Cloud energy storage double-layer optimization control method of competition condition generation type countermeasure network |
| CN114564815A (en) * | 2022-01-14 | 2022-05-31 | 重庆邮电大学 | Power system reliability and economy modeling method based on Brazilian paradox effect |
| CN117314598A (en) * | 2023-11-30 | 2023-12-29 | 深圳海辰储能科技有限公司 | Energy storage equipment lease capacity adjustment method and device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100179704A1 (en) * | 2009-01-14 | 2010-07-15 | Integral Analytics, Inc. | Optimization of microgrid energy use and distribution |
| CN107230088A (en) * | 2017-06-02 | 2017-10-03 | 张华� | A kind of lithium battery is reviewed and lease management system |
| CN108596464A (en) * | 2018-04-17 | 2018-09-28 | 南京邮电大学 | Electric vehicle based on dynamic non-cooperative games and cloud energy storage economic load dispatching method |
| CN109190882A (en) * | 2018-07-25 | 2019-01-11 | 南京邮电大学 | Microgrid economic optimization method of commerce under Power Market based on cloud energy storage |
| CN109245175A (en) * | 2018-11-21 | 2019-01-18 | 郑州大学 | A kind of large-scale wind power field energy storage capacity optimization method counted and ancillary service compensates |
-
2019
- 2019-05-15 CN CN201910404131.9A patent/CN110034561B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100179704A1 (en) * | 2009-01-14 | 2010-07-15 | Integral Analytics, Inc. | Optimization of microgrid energy use and distribution |
| CN107230088A (en) * | 2017-06-02 | 2017-10-03 | 张华� | A kind of lithium battery is reviewed and lease management system |
| CN108596464A (en) * | 2018-04-17 | 2018-09-28 | 南京邮电大学 | Electric vehicle based on dynamic non-cooperative games and cloud energy storage economic load dispatching method |
| CN109190882A (en) * | 2018-07-25 | 2019-01-11 | 南京邮电大学 | Microgrid economic optimization method of commerce under Power Market based on cloud energy storage |
| CN109245175A (en) * | 2018-11-21 | 2019-01-18 | 郑州大学 | A kind of large-scale wind power field energy storage capacity optimization method counted and ancillary service compensates |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111864770A (en) * | 2020-08-19 | 2020-10-30 | 国网河南省电力公司电力科学研究院 | Energy storage auxiliary frequency modulation scheduling method based on cloud energy storage |
| CN111864770B (en) * | 2020-08-19 | 2022-11-15 | 国网河南省电力公司电力科学研究院 | Energy storage auxiliary frequency modulation scheduling method based on cloud energy storage |
| CN112016824A (en) * | 2020-08-25 | 2020-12-01 | 国网四川省电力公司经济技术研究院 | Energy storage resource matching method based on shared economy concept |
| CN112016824B (en) * | 2020-08-25 | 2023-01-10 | 国网四川省电力公司经济技术研究院 | Energy storage resource matching method based on shared economy concept |
| CN113592553A (en) * | 2021-08-02 | 2021-11-02 | 广西大学 | Cloud energy storage double-layer optimization control method of competition condition generation type countermeasure network |
| CN113592553B (en) * | 2021-08-02 | 2023-10-17 | 广西大学 | Cloud energy storage double-layer optimization control method for competitive condition generation type countermeasure network |
| CN114564815A (en) * | 2022-01-14 | 2022-05-31 | 重庆邮电大学 | Power system reliability and economy modeling method based on Brazilian paradox effect |
| CN114564815B (en) * | 2022-01-14 | 2024-03-15 | 重庆邮电大学 | Power system reliability and economy modeling method based on Brazier paradox effect |
| CN117314598A (en) * | 2023-11-30 | 2023-12-29 | 深圳海辰储能科技有限公司 | Energy storage equipment lease capacity adjustment method and device and storage medium |
| CN117314598B (en) * | 2023-11-30 | 2024-03-12 | 深圳海辰储能科技有限公司 | Energy storage equipment lease capacity adjustment method and device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110034561B (en) | 2022-10-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | A coordinated dispatch method with pumped-storage and battery-storage for compensating the variation of wind power | |
| CN110034561A (en) | A kind of wind farm energy storage capacity optimization method based on cloud energy storage lease service | |
| Zakeri et al. | Policy options for enhancing economic profitability of residential solar photovoltaic with battery energy storage | |
| CN105846423B (en) | It is a kind of meter and demand response photovoltaic micro energy storage multiple target capacity collocation method | |
| Yang et al. | Optimal sizing and placement of energy storage system in power grids: A state-of-the-art one-stop handbook | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN112016747B (en) | Optimization method suitable for source-load-storage flexible resource overall planning and operation | |
| Luburić et al. | FACTS devices and energy storage in unit commitment | |
| CN109948849B (en) | Power distribution network frame planning method considering energy storage access | |
| CN109995063B (en) | User side energy storage control strategy | |
| CN109245175A (en) | A kind of large-scale wind power field energy storage capacity optimization method counted and ancillary service compensates | |
| CN110137955A (en) | A kind of decision-making technique counted and the robust Unit Combination of CVaR is dispatched | |
| Schwaegerl et al. | Quantification of technical, economic, environmental and social benefits of microgrid operation | |
| Li et al. | Optimal configuration of photovoltaic energy storage capacity for large power users | |
| CN114037191A (en) | Virtual power plant optimal scheduling method, device, equipment and medium based on big data | |
| Kong et al. | Greenplanning: Optimal energy source selection and capacity planning for green datacenters | |
| Cai et al. | Application of battery storage for compensation of forecast errors of wind power generation in 2050 | |
| Rahmati et al. | Pumped-storage units to address spinning reserve concerns in the grids with high wind penetration | |
| WO2023167631A1 (en) | An electrical power system and a multi-timescale coordinated optimization scheduling method therefor | |
| CN109829624B (en) | Wind power cooperative game climbing control method and device | |
| Du et al. | Optimal whole-life-cycle planning for battery energy storage system with normalized quantification of multi-services profitability | |
| Mohseni et al. | Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids | |
| Couture et al. | Residential prosumers: drivers and policy options (re-prosumers) | |
| CN117713068A (en) | User energy storage day-ahead optimal scheduling method and system considering application scene change | |
| Zalzar et al. | An incentive-based settlement mechanism for participation of flexible demands in day-ahead markets |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |